How do I create and plot a confusion matrix for my trained convolutional neural network?

I can't seem to create a confusion matrix for my validation accuracy outcome of my trained convolutional neural network. Below is the code I am using, and thanks in advance for any help!

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clear

rng('shuffle')

outputFolder = fullfile('D:\Large_grains\Training_set');

trainDigitData = imageDatastore(outputFolder,'IncludeSubfolders',true,'LabelSource','foldernames');

outputFolder = fullfile('D:\Large_grains\Validation_set');

testDigitData = imageDatastore(outputFolder,'IncludeSubfolders',true,'LabelSource','foldernames');

inputSize = [224 224 3];

augimdsTrain = augmentedImageDatastore(inputSize,trainDigitData,'ColorPreprocessing','gray2rgb');

augimdsValidation = augmentedImageDatastore(inputSize,testDigitData,'ColorPreprocessing','gray2rgb');

numClasses = 9;

problem2; % load ResNet-18

miniBatchSize = 32;

validationFrequency = floor(numel(trainDigitData.Labels)/miniBatchSize);

options = trainingOptions('sgdm',...

'LearnRateSchedule','piecewise',...

'LearnRateDropFactor',0.1,...

'LearnRateDropPeriod',2,...

'MaxEpochs',10,...

'InitialLearnRate',0.001,...

'MiniBatchSize',miniBatchSize,...

'ValidationData',augimdsValidation, ...

'ValidationFrequency',validationFrequency);

convnet = trainNetwork(augimdsTrain,lgraph,options);

[YPred] = classify(convnet,augimdsValidation);

plotconfusion(augimdsValidation.Labels,YPred)

[Shivam Singh]

Hello Steven,

Can you share what is error which you are facing with code? Also, can you share more information about the model ("lgraph") and the dataset used?

[Steven Mozarowski]

Thanks for your response, Shivam! I actually managed to have the script produce a confusion matrix earlier today and was meaning to take this post down when I saw your comment!

To answer your questions:

lgraph is a chart that shows information (like validation accuracy, epoch, time elapsed etc.) as training progresses.

The dataset is a pile of starch grain micrographs I had captured using an imaging flow cytometer. The images are organized in folders on a hard drive with 300 training and 200 validation images per species.

Thanks again for reaching out, I really appreciate it!

-Steven

Answer 1

[yanqi liu]

yes，sir，if want get the data information，may be use

[c,cm,ind,per] = confusion(augimdsValidation.Labels,YPred)